Complementarity-aware multi-parametric MR image feature fusion for abdominal multi-organ segmentation

T1-weighted in-phase and opposed-phase gradient-echo imaging is a routine component in abdominal MR imaging. Organ segmentation with the acquired images plays an important role in identifying various diseases and making treatment plans. Despite the promising performance achieved by existing deep learning models, further investigation is still needed to effectively exploit the information provided by different imaging parameters. Here, we propose a complementarity-aware multi-parametric MR image feature fusion network to extract and fuse the information of paired in-phase and opposed-phase MR images for enhanced abdominal multi-organ segmentation. Extensive experiments are conducted, and better results are achieved when compared to existing methods.